This article addresses the finite-time consensus tracking control problem for nonlinear multiagent systems (MASs), in which state variables are unmeasured and nonlinear functions are totally unknown. ...An observer is designed to estimate state variables and fuzzy-logic systems are employed to approximate nonlinearities. Then, an observer-based adaptive fuzzy consensus tracking controller is developed by using the backstepping technique and constructing a novel barrier Lyapunov function with the consideration of the characteristics of MASs. The proposed control protocol can guarantee that: 1) all signals in the closed-loop system keep bounded and 2) the consensus tracking error converges to a prespecified region of the origin in the prescribed finite time. Compared with the existing observer-based finite/fixed-time control protocols, the settling time and the convergence region in our work can be both preassigned by the designer and not affected by the unknown positive constant, which lies in the Lyapunov derivative inequality. Finally, two comparison simulation examples, including a numerical example and a practical example, check the availability of the designed control scheme.
In this article, the dynamic event‐triggered model‐free adaptive security tracking control problem of the subway system with speed and traction/braking force constraints under aperiodic ...denial‐of‐service (DoS) attacks is studied. The complex subway train system is transformed into a linearization data model by using the dynamic linearization technique. For the subway system, both speed and traction/braking forces need to be restrained to ensure the safety of the subway equipment. In addition, the dynamic event‐triggered mechanism is designed to reduce communication frequency and prolong the hardware life cycle. At the same time, considering aperiodic DoS attacks in the network channel, the security tracking control problem is guaranteed. In the simulation, the algorithm is applied to the subway train system, and the effectiveness of this algorithm is verified with a comparison.
Conventional approaches to diagnosing Parkinson’s disease (PD) and rating its severity level are based on medical specialists’ clinical assessment of symptoms, which are subjective and can be ...inaccurate. These techniques are not very reliable, particularly in the early stages of the disease. A novel detection and severity classification algorithm using deep learning approaches was developed in this research to classify the PD severity level based on vertical ground reaction force (vGRF) signals. Different variations in force patterns generated by the irregularity in vGRF signals due to the gait abnormalities of PD patients can indicate their severity. The main purpose of this research is to aid physicians in detecting early stages of PD, planning efficient treatment, and monitoring disease progression. The detection algorithm comprises preprocessing, feature transformation, and classification processes. In preprocessing, the vGRF signal is divided into 10, 15, and 30 s successive time windows. In the feature transformation process, the time domain vGRF signal in windows with varying time lengths is modified into a time–frequency spectrogram using a continuous wavelet transform (CWT). Then, principal component analysis (PCA) is used for feature enhancement. Finally, different types of convolutional neural networks (CNNs) are employed as deep learning classifiers for classification. The algorithm performance was evaluated using k-fold cross-validation (kfoldCV). The best average accuracy of the proposed detection algorithm in classifying the PD severity stage classification was 96.52% using ResNet-50 with vGRF data from the PhysioNet database. The proposed detection algorithm can effectively differentiate gait patterns based on time–frequency spectrograms of vGRF signals associated with different PD severity levels.
The HfNbTaTiZr refractory high-entropy alloy was investigated on the grain growth kinetics and tensile properties. Grain growth at 1200–1350°C is rather slow. The activation energy is 389kJ/mol and ...the growth exponent is 3.5. The HfNbTaTiZr alloy has high strength, small work hardening and high ductility. Grain refining is found to enhance the tensile strength and ductility simultaneously.
•The HfNbTaTiZr alloy exhibits low rate and high activation energy of grain growth.•The slow grain boundary migration is a result of the solute-drag mechanism.•Grain refinement simultaneously increases tensile strength and ductility•The alloy with a small grain size has excellent tensile yield strength and ductility.
Electrocardiographic imaging (ECGi) reconstructs electrograms at the heart's surface using the potentials recorded at the body's surface. This is called the inverse problem of electrocardiography. ...This study aimed to improve on the current solution methods using machine learning and deep learning frameworks. Electrocardiograms were simultaneously recorded from pigs' ventricles and their body surfaces. The Fully Connected Neural network (FCN), Long Short-term Memory (LSTM), Convolutional Neural Network (CNN) methods were used for constructing the model. A method is developed to align the data across different pigs. We evaluated the method using leave-one-out cross-validation. For the best result, the overall median of the correlation coefficient of the predicted ECG wave was 0.74. This study demonstrated that a neural network can be used to solve the inverse problem of ECGi with relatively small datasets, with an accuracy compatible with current standard methods.
Although refractory high-entropy alloys have exceptional strength at high temperatures, they are often brittle at room temperature. One exception is the HfNbTaTiZr alloy, which has a plasticity of ...over 50% at room temperature. However, the strength of HfNbTaTiZr at high temperature is insufficient. In this study, the composition of HfNbTaTiZr is modified with an aim to improve its strength at high temperature, while retaining reasonable toughness at room temperature. Two new alloys with simple BCC structure, HfMoTaTiZr and HfMoNbTaTiZr, were designed and synthesized. The results show that the yield strengths of the new alloys are apparently higher than that of HfNbTaTiZr. Moreover, a fracture strain of 12% is successfully retained in the HfMoNbTaTiZr alloy at room temperature.
•Both HfMoTaTiZr and HfMoNbTaTiZr alloys have simple BCC structure.•The elevated temperature properties and microstructure evolution of both alloys are investigated.•HfMoNbTaTiZr has better combination of strength and plasticity than HfMoTaTiZr.•The yield strength of HfMoNbTaTiZr is six times that of HfNbTaTiZr at 1200 °C.•HfMoTaTiZr and HfMoNbTaTiZr have great potential in high-temperature applications.
Efficient nanotextured black silicon solar cells passivated by an Al2O3 layer are demonstrated. The broadband antireflection of the nanotextured black silicon solar cells was provided by fabricating ...vertically aligned silicon nanowire (SiNW) arrays on the n + emitter. A highly conformal Al2O3 layer was deposited upon the SiNW arrays by the thermal atomic layer deposition (ALD) based on the multiple pulses scheme. The nanotextured black silicon wafer covered with the Al2O3 layer exhibited a low total reflectance of ∼1.5% in a broad spectrum from 400 to 800 nm. The Al2O3 passivation layer also contributes to the suppressed surface recombination, which was explored in terms of the chemical and field-effect passivation effects. An 8% increment of short-circuit current density and 10.3% enhancement of efficiency were achieved due to the ALD Al2O3 surface passivation and forming gas annealing. A high efficiency up to 18.2% was realized in the ALD Al2O3-passivated nanotextured black silicon solar cells.
To diagnose neurodegenerative diseases (NDDs), physicians have been clinically evaluating symptoms. However, these symptoms are not very dependable-particularly in the early stages of the diseases. ...This study has therefore proposed a novel classification algorithm that uses a deep learning approach to classify NDDs based on the recurrence plot of gait vertical ground reaction force (vGRF) data. The irregular gait patterns of NDDs exhibited by vGRF data can indicate different variations of force patterns compared with healthy controls (HC). The classification algorithm in this study comprises three processes: a preprocessing, feature transformation and classification. In the preprocessing process, the 5-min vGRF data divided into 10-s successive time windows. In the feature transformation process, the time-domain vGRF data are modified into an image using a recurrence plot. The total recurrence plots are 1312 plots for HC (16 subjects), 1066 plots for ALS (13 patients), 1230 plots for PD (15 patients) and 1640 plots for HD (20 subjects). The principal component analysis (PCA) is used in this stage for feature enhancement. Lastly, the convolutional neural network (CNN), as a deep learning classifier, is employed in the classification process and evaluated using the leave-one-out cross-validation (LOOCV). Gait data from HC subjects and patients with amyotrophic lateral sclerosis (ALS), Huntington's disease (HD) and Parkinson's disease (PD) obtained from the PhysioNet Gait Dynamics in Neurodegenerative disease were used to validate the proposed algorithm. The experimental results included two-class and multiclass classifications. In the two-class classification, the results included classification of the NDD and the HC groups and classification among the NDDs. The classification accuracy for (HC vs. ALS), (HC vs. HD), (HC vs. PD), (ALS vs. PD), (ALS vs. HD), (PD vs. HD) and (NDDs vs. HC) were 100%, 98.41%, 100%, 95.95%, 100%, 97.25% and 98.91%, respectively. In the multiclass classification, a four-class gait classification among HC, ALS, PD and HD was conducted and the classification accuracy of HC, ALS, PD and HD were 98.99%, 98.32%, 97.41% and 96.74%, respectively. The proposed method can achieve high accuracy compare to the existing results, but with shorter length of input signal (Input of existing literature using the same database is 5-min gait signal, but the proposed method only needs 10-s gait signal).
To apply high-entropy alloys (HEAs) for extensive advanced structural uses, their welding properties should be well understood. In this study, Al0.3CoCrCu0.3FeNi HEA was butt welded by friction stir ...welding (FSW). The fine-grained partially recrystallized microstructure in the stir zone gave rise to a high tensile yield strength of 920 MPa with an elongation of 37%. By microstructural observation, the excellent mechanical properties of the stir zone material were attributed to the partially recrystallized heterogeneous structure, with which the synergetic strengthening improved the strength of the HEA with considerably less trade-off in its ductility. This unique phenomenon was unprecedented in any other friction-stir welded conventional alloys and was credited to the low stacking-fault energy and the high grain growth activation energy of the HEA. This work suggests that FSW can not only produce good HEA welds but serve as a special processing technique to enhance mechanical properties of HEAs.
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•The partially recrystallized heterogeneous microstructure produced by friction stir welding leads to strong and ductile high-entropy alloys.•Friction stir welding can serve as a novel processing technique for high-entropy alloys provided their conduciveness to heterogeneities.•Low stacking fault energy and high grain growth activation energy facilitate the formation of heterogeneous structures during friction stir welding.
•The study developed a series of refractory AlxHfNbTaTiZr HEAs.•The aim to improve strength, and reduce density of the very ductile base alloy.•These HEAs mainly consist of a simple BCC solid ...solution.•The addition of Al significantly improved the strength characteristics.•Solution hardening led to a decrease in ductility.
This study developed a series of refractory AlxHfNbTaTiZr high-entropy alloys (HEAs) with an aim to improve strength, and reduce density of the very ductile base alloy HfNbTaTiZr. Despite the diversity of crystal structures among the constituent elements, all the HEAs are single solid solution phase with body-centered cubic (BCC) structure. The addition of Al significantly improves the strength but reduces the ductility due to large solution hardening. The linear relation between yield strength and atomic percentage of Al suggests that the strengthening effect of a certain element in a single-phase HEA alloy can be explained based on quasi-binary alloy concept. Crack formations in deformed AlHfNbTaTiZr alloy with the lowest fracture strain are mainly along the boundaries between dendrite and interdendrite. This agrees with its large deviation of Al content and thus strength between dendrite and interdendrite.
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